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Long memory and nonlinearities in realized volatility: a Markov switching approach

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  • S. Bordignon
  • D. Raggi

Abstract

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.

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Paper provided by Dipartimento Scienze Economiche, Universita' di Bologna in its series Working Papers with number 694.

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Date of creation: Feb 2010
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Handle: RePEc:bol:bodewp:694

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Cited by:
  1. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It's all about volatility of volatility: evidence from a two-factor stochastic volatility model," Studies in Economics, Department of Economics, University of Kent 1404, Department of Economics, University of Kent.
  2. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
  3. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 339-358.
  4. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, School of Economics and Management, University of Aarhus.
  5. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers, University of Milano-Bicocca, Department of Economics 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
  6. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 618-641.
  7. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
  8. Shinichiro Shirota & Takayuki Hizu & Yasuhiro Omori, 2013. "Realized Stochastic Volatility with Leverage and Long Memory," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-880, CIRJE, Faculty of Economics, University of Tokyo.
  9. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers, Dipartimento di Scienze Economiche "Marco Fanno" 0138, Dipartimento di Scienze Economiche "Marco Fanno".
  10. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, School of Economics and Management, University of Aarhus.
  11. Nima Nonejad, 2013. "Time-Consistency Problem and the Behavior of US Inflation from 1970 to 2008," CREATES Research Papers 2013-25, School of Economics and Management, University of Aarhus.

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